3D Semantic Segmentation With Material Identification for Object Meshes
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Solution Overview
Problem
Existing methods struggle to accurately segment 3D objects into semantically meaningful parts and identify the materials used in their construction, which is crucial for applications such as robotics and autonomous systems.
Innovation Solution
A multi-step process leveraging 2D segmentation neural networks like Segment Anything Model (SAM) for initial segmentation, Contrastive Language-Image Pretraining (CLIP) image encoders for feature mapping, and large language models (LLMs) for material identification, combined with optimization algorithms to refine features and ensure accurate clustering, is employed to separate and identify materials in 3D objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional segmentation methods are used for 3D objects, then the process is simpler, but the segmentation accuracy and semantic meaningfulness deteriorate
Solution Approach 1:
The patent divides the complex task of 3D object segmentation into multiple sequential steps: initial 2D segmentation using SAM, feature mapping through CLIP, optimization refinement, and material identification using LLMs. This multi-stage segmentation approach breaks down the complex problem into manageable components, achieving high segmentation accuracy while organizing the complexity in a structured manner
Solution Approach 2:
The patent introduces intermediate representations and models between the input 3D mesh and final segmentation output. Specifically, it uses 2D segmentation masks as intermediaries, CLIP feature maps as intermediate features, and optimization algorithms as intermediate processing steps. These intermediaries bridge the gap between simple input and complex output requirements
2Measurement precision
If simple material identification methods are used, then the system is easier to implement, but the material identification accuracy deteriorates
Solution Approach 1:
The patent performs preliminary actions by first segmenting the 3D object into meaningful parts using 2D segmentation and feature mapping before attempting material identification. This preliminary segmentation provides clean, organized input for the subsequent material identification stage, improving accuracy while managing complexity through staged processing
Solution Approach 2:
The patent transforms the 3D mesh into different parameter representations including 2D images, feature vectors, and optimization variables. By changing parameters and representations at different stages, the system can leverage specialized models (SAM for segmentation, CLIP for features, LLMs for materials) to achieve high accuracy in each specific task
3Manufacturing precision
If detailed semantic segmentation is performed, then the object representation is more accurate, but the processing time increases
Solution Approach 1:
The patent segments the processing task into parallelizable stages where 2D segmentation, feature mapping, and optimization can be performed efficiently. By dividing the detailed semantic segmentation into discrete steps, the system can optimize each step independently and potentially parallelize computations to reduce overall processing time while maintaining high representation accuracy
Data Source
AI summary
Apparatuses, systems, and techniques to generate a 3D segmentation mask using 3D data representing an object. In at least one embodiment, the 3D segmentation mask identifies different parts of the object and/or properties associated with at least a portion of the parts of the object (e.g., one or more materials from which a surface of the object is constructed). In at least one embodiment, part(s) and/or material(s) of a 3D object are identified using two or more neural networks that perform 2D semantic segmentation, and feature mapping.